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Activity Number:
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492
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Type:
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Invited
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Date/Time:
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Thursday, August 2, 2007 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistical Computing
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| Abstract - #308123 |
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Title:
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Bayesian Inference for a Longitudinal Social Network Model
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Author(s):
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Mark S. Handcock*+
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Companies:
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University of Washington
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Address:
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Department of Statistics, Seattle, WA, 98195-4322,
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Keywords:
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random graph models ; stochastic process ; MCMC
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Abstract:
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In studies of social networks recent emphasis has been placed on random graph models where the nodes represent individual social actors and the edges represent a specified relationship between the actors. Much progress has been made on exponential family models for cross-sectional networks, and some has been made on related models for networks observed longitudinally. A fundamental goal of social network theory is to represent the processes of network formation over time. The theory of balance developed by Heider posits that networks evolve towards structural balance. An alternative theory due to Simmel has the triad as the basic social unit. We develop Bayesian inference for a model that represents the level and dynamics of Heiderian balance and Simmelian stability, and access the empirical support for the two theories. This is joint work with David Krackhardt and Martina Morris.
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- The address information is for the authors that have a + after their name.
- Authors who are presenting talks have a * after their name.
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